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Cigarette smoking and risk of uterine myoma: systematic review and meta-analysis
European Journal of Obstetrics & Gynecology and Reproductive Biology 197 (2016) 63–71
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Cigarette smoking and risk of uterine myoma: systematic review and meta-analysis Francesca Chiaffarino a,*, Elena Ricci a, Sonia Cipriani a, Vito Chiantera b, Fabio Parazzini a,c a
Dipartimento della donna, del bambino e del neonato, Fondazione IRCCS Ca` Granda, Ospedale Maggiore Policlinico, Milan, Italy Department of Gynecology, Charite` Universita¨tsmedizin, Charitestraße 1, 10117 Berlin, Germany c Dipartimento di Scienze Cliniche e di Comunita`, Universita` degli studi di Milano, Milan, Italy b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 22 June 2015 Received in revised form 11 November 2015 Accepted 18 November 2015
Objective: To review the literature on the relationship between smoking and the risk of uterine myoma, we conducted a systematic review and a meta-analysis of published studies. In this meta-analysis, we included all identified studies of association between smoking and uterine myoma where these were case–control or cohort studies, reporting original data, ultrasound or histological confirmed diagnosis of myomas and information on the association between tobacco smoking and myomas. Study design: We carried out a literature search on MEDLINE/EMBASE of all studies published as original articles in English up to October 2015, using the Medical Subject Heading terms and free search terms about myoma and smoking. We selected only studies published in English. Moreover, bibliographies of the retrieved papers were reviewed, to identify any other relevant publication. A total of 14 different studies were eligible for a qualitative synthesis and data extract from 10 studies were combined in a meta-analysis. Results: The summary OR of former compared to never smokers was 0.93 (0.88–0.99) with no heterogeneity. The summary OR of current smokers compared to never smokers, was 0.83 (0.65–1.04), even if the subtotal OR in cohort studies was 0.85 (0.73–0.98) with no heterogeneity. When sensitivity analysis was performed the summary OR was 0.83 (0.71–0.97). Conclusion: The primary meta-analyses found no significant effect of smoking on risk of uterine myoma. Subgroup analysis for study design showed a small risk reduction for current and former smokers in cohort studies. A sensitivity analysis showed an inverse association between ever smoking and uterine myoma. However, given the limited number of studies in each sub-analysis, weak associations and the absence of a dose dependent effect, caution should be paid in the interpretation of these findings and further investigation are needed. ß 2015 Elsevier Ireland Ltd. All rights reserved.
Keywords: Smoking Uterine myoma Risk factor
Introduction Uterine myomas are the most common benign tumors derived from smooth muscle cells in the uterine myometrium. In an online survey the self-reported prevalence of myoma, in the age group of 40–49 years, ranged from 9.4% in United Kingdom to 17.4% in Italy [1], but many tumors are asymptomatic and may not be diagnosed. Although the etiology of uterine myoma is still not well known,
* Corresponding author at: Via Commenda, 12-20122 Milano, Italy. Tel.: +39 02 55032318; fax: +39 02 550320252. E-mail address: [email protected] (F. Chiaffarino). http://dx.doi.org/10.1016/j.ejogrb.2015.11.023 0301-2115/ß 2015 Elsevier Ireland Ltd. All rights reserved.
advances have been made in the understanding of the hormonal factors, genetic factors and growth factors of these tumors [2]. They have considered a hormonal-dependent pathological condition, where growth is thought to depend on ovarian hormones. Both estrogen and progesterone appear to promote the development of myomas. Factors that increase exposure to estrogen, such as obesity and early menarche, increase the incidence [2]. On the other hand, exercise and increased parity, which decreased exposure to estrogen, appear to be protective [3]. Smoking is a modifiable risk factor that may affect endogenous levels of hormones and women who smoke have lower urinary estrogen levels during the luteal phase of the menstrual cycle than non-smokers [4]. Nicotine can reduce androgens conversion to
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estrone secondary to inhibition of aromatase [5]. Thus, smoking is associated with impaired production and reduced levels of endogenous circulating estrogens [6]. Epidemiological studies investigating the role of tobacco smoking have shown conflicting results: some have shown an inverse relationship between cigarette smoking and risk of uterine myoma [7–10], but in others smoking increased the risk [11,12], whereas in two cohort studies it was unrelated to myoma risk [13,14]. Thus, in order to provide a summary of the available literature on the relation between cigarette smoking and uterine myoma, we conducted a systematic review and to allow an overall quantitative estimate of any such relation, we combined in a meta-analysis all published data on the issue. Materials and methods The review and the meta-analysis were performed according to PRISMA (preferred reporting items for systematic reviews and meta-analyses) [15] and MOOSE (Meta-analysis of Observational Studies in Epidemiology) guidelines [16]. We executed a MEDLINE/EMBASE search of papers published until October 10, 2015, using the Medical Subject Heading terms in free research ‘‘myoma’’ or ‘‘leiomyoma’’ combined with ‘‘smoking’’ and ‘‘tobacco’’ and free search terms ‘‘tobacco’’ or ‘‘smoking’’ or ‘‘smok*’’ or ‘‘cigarette*’’ in combination with ‘‘fibroids’’ or ‘‘uterine fibroids’’ or ‘‘myoma’’ or ‘‘uterine myoma’’ or ‘‘leiomyoma’’ or ‘‘uterine leiomyoma’’. We selected only studies on humans, published as full-length papers in English. Moreover, bibliographies of the retrieved papers were reviewed, to identify any other relevant publication. In the review we included all identified studies of association of smoking and uterine myoma, whereas studies were included in the meta-analysis only if: they were case–control or cohort studies, reporting original data; diagnosis of myomas was ultrasound or histological confirmed and/or clinically based; studies reported information on the association between tobacco smoking and myomas, including estimates of the relative risk (RR) or the odds ratio (OR), with the corresponding 95% confidence interval (CI), or frequency distribution to calculate them. When we found more than one publication based on the same study population and data, we included only the one with most detailed information, or published most recently. Data extraction and selection of eligible studies was carried out in duplicate by two investigators (FC and ER). Disagreements were solved by discussing and reviewing the respective issue. Crossreferencing of selected articles revealed no further eligible records. From each publication we extracted the following information: country of origin; study design; number and characteristics of subjects (cases, controls or cohort size); age, if available; categories of tobacco smoking (smoking status, smoking intensity and duration of smoking, if available); measures of association (RR or OR) of myomas and corresponding 95% CI for every category of tobacco smoking, or frequency distribution to calculate them; confounding variables allowed for in the statistical analysis. When more than one regression model was provided, estimates adjusted for the largest number of confounding variables were considered. The quality of the studies included in the meta-analysis were assessed using the Newcastle–Ottawa scale. This instrument was developed to assess the quality of nonrandomized studies, specifically cohort and case–control studies [17]. Studies were judged based on three broad categories: selection of study groups, comparability of study groups, and assessment of outcome (cohort studies) or ascertainment of exposure (case–control studies). Maximum score was 9.
For some studies, we pooled estimates of different categories of cases or controls using the method by Hamling et al. [18], thus taking into account their correlation. Pooled estimates of the odds ratios (OR) and the corresponding 95% confidence intervals (CI) were calculated using fixed or, when significant heterogeneity among estimates emerged, random effects models. Sensitivity analysis were also performed. We assessed the heterogeneity among studies using the x2 test [19] and quantified it using the I2 statistic, which represents the percentage of the total variation across studies that is attributable to heterogeneity rather than chance [20]. Results were defined as heterogeneous for p values less than 0.10 [19]. We computed summary estimates for ever tobacco smokers, former smokers, current smokers, moderate current smokers, and heavy current smokers, as compared to never smokers. Among the selected studies, six reported more categories of current smokers, thus we could calculate separate estimates for moderate and heavy current smokers but we were able to combine data from four studies because two studies considered ever smokers and not only current ones. Moreover, different cut-points for moderate and heavy smoking were chosen, depending on those shown in the papers: thus the cut-point for moderate smoking was less than 10 cigarettes per day in two studies [8,14], less than 15 cigarettes per day in one study [13] and less than 1 pack/day in another [11]. For heavy current smokers the cut-point was more than 19 cigarettes per day in two studies [11,14], more than 24 in one study [13] and more than 10 in another one [8]. Publication bias was evaluated using funnel plot [21]. Results From the literature search we identified 345 articles, after the exclusion of 170 as duplicates. 331 studies were excluded for the reason shown in Fig. 1 and 14 articles describing 14 different studies were eligible for a qualitative synthesis and data extract from 10 studies were combined in a meta-analysis. The main characteristics of identified papers are presented in Table 1: eight case control studies, four cohort studies and two cross-sectional. Of the selected studies, 8 were from USA, 3 from Europe and 3 from Asia. The articles were published between 1986 and 2012. The effect estimates according to smoking exposure published in the selected articles were summarized in Table 2. In the meta-analysis we excluded two cross-sectional studies [12,22], since in this study design exposure and disease are recorded at the same time: we could not determine whether the exposure preceded the occurrence of uterine myoma. Moreover, two studies were excluded because the categories of smoking exposure were not clear [23,24] and in the American cohort study the presence of myoma was self-reported without any other diagnosis confirmation [24]. Overall, data from ten studies, including 374,212 women, 7612 with uterine myoma, were used in the meta-analysis. Ever smokers In qualitative analysis seven studies reported information on ever smokers (Table 2). Among these, three of them, two case– control studies and one cohort study, showed no effect of ever smoking. Two case–control studies showed a protective effect of ever smoking [7,9] and in the American study was dose dependent [9] whereas the exposure to cigarette smoking increased the risk of myoma in Iranian premenopausal women and in Slovenian women [23,25]. In quantitative analysis the Iranian study was excluded because the categories of smoking exposure were not clear. In the random
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Fig. 1. Flow chart of the selection of studies on cigarette smoking and risk of uterine myoma included in the systematic review and meta-analysis.
effects model of the meta-analysis, we considered ten studies. Only two studies [7,26] reported the adjusted OR of ever smokers compared to never smokers; in the study by Pakiz we calculated the OR from published frequency distribution; seven studies did not compare ever versus never smokers, thus we calculated the OR or RR, as appropriate, combining former and current smokers or more categories of ever smokers in one ever smokers category. Consequently, these estimates were not adjusted. The summary OR (95% CI) of myoma for ever smokers, compared to never smokers, was 0.86 (0.73–1.01) with significant heterogeneity (x2 = 76.92, p = 0.00001), similarly to both OR obtained from cohort and from case–control studies separately, as shown in Fig. 2. Moreover, as regards the case–control studies on the relationship between ever smoking and uterine myoma, it becomes immediately evident, that the OR in the study by Pakiz [25] was completely different. It should take into account that this Slovenian study [25] regarded a small sample of women with higher age range when compared with other studies and the estimate, included in the meta-analysis, was not adjusted. Furthermore, tobacco smoking was not the main topic of the paper. In sensitivity analysis, when a
Slovenian study [25] was dropped, the OR of myoma for ever smokers in case–control studies subgroup was 0.75 (0.62–0.92), and the summary OR for ever smokers as compared to never smokers was 0.83 (0.71–0.97) with significant heterogeneity (x2 = 18.02, p = 0.003). Current smokers A cohort study [10] and a case–control study [8], including preand post-menopausal women, showed an inverse association between myoma and smoking. On the contrary, in a Chinese case– control study, current smoking of one or more packs of cigarettes per day was associated with an increasing risk for White women, but not for African-American women [11]. Moreover, in a crosssectional study, smoking was positively associated with diffuse myomas, with similar patterns between African-American and Caucasians women, but was not associated with submucosal or intramural/subserosal myoma [12]. In a case–control [27] and two cohort studies [13,14] current smoking was not associated with risk of uterine myoma (Fig. 3).
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Table 1 Main characteristics of the studies on tobacco smoking and risk of uterine myoma. Country
Study design
Cases
Controls
Sample size cases/ controls
Age (ys)
Smoking habit
Bidgoli et al., 2012
Iran
Case–control
Women with myomas confirmed by pathological reports
Women without myomas
138/138
Postmenopausal women were excluded.
Chen et al., 2001
USA
Case–control
Women with myomas undergoing tubal sterilization
Women without myomas undergoing tubal sterilization
White: 247–988, African-American: 70– 280 cases and controls respectively
Dragomir et al., 2010
USA
Cross-sectional
Women with myomas (ultrasound confirmed)
Women without myomas
Faerstein et al., 2001
USA
Case–control
Women with myomas (surgically or sonographically confirmed)
Women without myomas
Caucasian: 203–202, African-American: 419–162 cases and controls respectively 318/394
<44 were included. White: 35.6–32.0 African-American: 34.1–29.5 cases and controls respectively 35–49
Active and passive exposure to cigarettes smoke versus no exposure Never, former smoker, current smoker smoking < or >1 cigarettes pack/day
Lambertino et al., 2011
USA
Cohort
Women with self reported myomas
Women without myomas
95 cases
Lumbiganon et al., 1995
Thailand
Case–control
Women without myomas
910/2709
Marshall et al., 1998
USA
Cohort
Women with myomas who required surgical treatment (histopathologically confirmed) Women with myomas (ultrasound/ hysterectomy confirmed)
Women without myomas
2967 new cases among 94,095 premenopausal women
Nagata et al., 2009
Japan
Cross-sectional
Women without myomas
Pakiz et al., 2010
Slovenia
Case–control
Parazzini et al., 1995
Italy
Case–control
Women undergoing vaginal hysterectomy for pelvic organ prolapse Women admitted to the hospitals for acute, nongynecologic, nonhormonal, nonneoplastic conditions
Women with myomas (ultrasound/ hysterectomy confirmed) Women with myomas (histologically confirmed)
Women with myomas (histologically confirmed)
42.3 6.4 20–55 cases; 39.8 6.0 18–53 controls. Postmenopausal women were excluded. Mean age women with myoma: 54.7. w/o myoma:52.7 87.5% and 82.7% cases and controls respectively were <50 age.
Never, ever smokers
Never, former, current smoker. Tertile of duration and pack/ years
Confounding factors
NOS Quality score
Age at sterilization and number of living children, education, heavy menstrual flow, irregular cycles, cycle length Age, age at menarche, full term pregnancies, BMI, Physical activity, ethnicity Age, clinic, etnicity, education and marital status
6
6
% cigarette smoking
N.A.
never, ever smokers
Control for potential counfounding factors, but not otherwise specified
6
25–42
Never, current, former smokers, n. cigarettes/ day
8
54/231
Postmenopausal women were excluded
Never, current, former smokers
Age, race, marital status, age at menarche, BMI, age at first birth, years since last birth, history of infertility and age at first oral contraceptive use N.A.
50 + 56/41
Age range. Cases: 42.4–53.5 Controls: 57.2–64.2
smoking and duration of smoking (mean)
N.A.
5
476/1283
<55 years
Never, current, former smokers, no. of cigarettes/day, duration of smoking (years)
Age, education, menopausal status, BMI, parity, oral contraceptive use
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Study and year
8 Age, time period, age at menarche, parity, age at first and last birth, education, BMI, OC use
6
Never, former, current smoker. Never, former, current smoker. Cigarettes/day, pack/years, y. of smoking, y. since quitting 25–84
1790 cases among 133,000 women 2177 cases Wise et al., 2004
Cohort USA
Templeman et al., 2009
Cohort USA
Samadi et al., 1996
Case–control
Women with myomas (surgically confirmed) Women with myomas (ultrasound or hysterectomy confirmed)
Women without myomas Women without myomas
Only premenopausal women
6 20–54 201/1503 Women without myomas
Never, ever
25–39 535/535 Ross et al., 1986
England and Scotland USA
Case–control
Women with myomas (pathologically confirmed) Women with selfreported myomas with physician clinical diagnosis
Women without myomas
No. of cigarettes/day
Menopausal status, frequency pap test, age at menarche, education, breastfeeding, race, BMI, oral contraceptive use
6
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In the meta-analyses of three cohort and three case–control studies combined, the summary OR of current smokers was 0.83 (0.65–1.04) with significant heterogeneity (x2 = 24.48, p = 0.0004). The subtotal OR from cohort studies was 0.85 (0.73–0.98) with no heterogeneity (x2 = 3.60, p = 0.16). If data were published in classes of cigarettes per day or pack/years, we included the estimates of the highest dose class in the analyses. Former smokers Six studies reported information on former smokers (Table 2) and only a cohort study [10] found a small significant protective effect of former smoking on myoma risk. In the meta-analysis the summary OR of former compared to never smokers (see Fig. 4) was 0.93 (0.88–0.99) with not significant heterogeneity (x2 = 4.03, p = 0.67). Moderate and heavy current smokers Among the selected studies, six reported more categories of current smokers, thus we could calculate separate estimates for moderate and heavy current smokers but we were able to combine data from four studies because two studies considered ever smokers and not only current ones. Moreover, different cut-points for moderate and heavy smoking were chosen, depending on those shown in the papers (see Materials and Methods). Fig. 5A and B reported respectively the results of the metaanalyses of low-moderate (including the risk estimates for the lowest class) and heavy current smokers (including the estimates for the highest class) versus never smokers: both ORs were not statistically significant with borderline heterogeneity. Duration of smoking Only three studies reported effect measures for the relationship between duration of smoking and myoma risk. Two of them found no association, whereas no clear association emerged between years of smoking and risk of myoma in our previous [8]: women with less than twenty years of smoking, had approximately a 50% reduced risk of myoma requiring surgery, but this protection was not found in women with twenty or more years of smoking. Since information on smoking habit duration was even more heterogeneous than that on cigarettes dose, we decided not to try a quantitative synthesis. Funnel plot, reported in Fig. 6, is a graphical tool for the assessment of publication bias. Its interpretation is largely subjective [28] and can be supported by Egger’s test for assessing asymmetry. In the present study the Egger’s test result (p = 0.35) suggested no presence of publication bias thought, because of the lower power of the test, bias cannot be excluded [29]. Comment Epidemiological studies investigating the role of smoking on risk of myoma have shown conflicting results but to our knowledge, this is the first systematic review and meta-analysis on that topic. The primary meta-analysis found no significant effect of smoking on risk of myoma. Subgroup analysis for study design showed a small, but significant, risk reduction for current and former smokers in cohort studies. The absence of statistical significance in ever smokers and in the subgroup of case–control studies could be due to the analysis, which was based, in most studies, in absence of adjusted OR, on raw estimates. Moreover, as regards the case– control studies on the relationship between ever smoking and myoma, a sensitivity analysis, dropping the study by Pakiz [25], showed an inverse association between ever smoking and myoma.
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Table 2 Reported estimates in selected studies. Comparison
First author
Year
Study design
Published estimates (95% CI) All
Samadi [a] Lumbiganon [b] Bidgoli [c] Pakiz [d] Ross [e] Ross [e] Faerstein [f] Faerstein [f] Faerstein [f] Wise [g] Wise [g] Wise [g] Wise [g]
1996 1996 2012 2010 1986 1986 2001 2001 2001 2004 2004 2004 2004
Case–control Case–control Case–control Case–control Case–control Case–control Case–control Case–control Case–control Cohort Cohort Cohort Cohort
0.8 (0.5–1.1) 0.63 (0.40–0.99) 2.09 (1.29–3.37) 0.198 (0.075–0.52) 0.74 0.65 1.0 (0.6–1.6) 0.8 (0.5–1.4) 0.9 (0.5–1.4)
Faerstein [f] Dragomir [h] Dragomir [h] Dragomir [h] Wise [g] Templeman [i] Parazzini [l] Parazzini [l] Marshall [m] Marshall [m] Marshall [m] Chen [n] Chen [n] Wise [g] Wise [g] Wise [g]
2001 2010 2010 2010 2004 2009 1996 1996 1998 1998 1998 2001 2001 2004 2004 2004
Case–control Cross-sectional Cross-sectional Cross-sectional Cohort Cohort Case–control Case–control Cohort Cohort Cohort Case–control Case–control Cohort Cohort Cohort
0.7 (0.4–1.3) 2.13 (1.29–3.53) 1.24 (0.74–2.07) 0.87 (0.58–1.32)
Parazzini [l] Chen [n] Faerstein [f] Wise [g] Templeman [i] Marshall [m] Marshall [m]
1996 2001 2001 2004 2009 1998 1998
Case–control Case–control Case–control Cohort Cohort Cohort Cohort
Parazzini Parazzini Faerstein Faerstein Faerstein Wise [g] Wise [g] Wise [g]
1996 1996 2001 2001 2001 2004 2004 2004
Case–control Case–control Case–control Case–control Case–control Cohort Cohort Cohort
Published estimates (95% CI) White women
Published estimates (95% CI) African-American women
Ever versus never smokers
1–14 cigarettes/day 15 cigarettes/day <5 pack/year 5–13 pack/year 13.2–58 pack/year <2.0 pack/year 2.0–5.9 pack/year 6.0–12.4 pack/year 12.5 pack/year
0.94 0.97 0.88 0.93
(0.79–1.12) (0.81–1.16) (0.73–1.04) (0.79–1.11)
1.97 1.13 0.89 0.88
(1.11–3.51) (0.64–2.01) (0.56–1.40) (0.77–1.01)
Current versus never smokers
1–9 cigarettes/day 10 cigarettes/day 1–14 cigarettes/day 15–24 cigarettes/day 25 cigarettes/day <1 pack/day 1 pack/day <10cigarettes/day 10–19 cigarettes/day 20 cigarettes/day
3.00 (1.07–8.38) 1.60 (0.52–4.98) 0.63 (0.23–1.74)
0.65 (0.50–0.85) 0.5 (0.3–0.7) 0.5 (0.4–0.8) 0.84 (0.70–1.01) 0.89 (0.75–1.06) 0.93 (0.73–1.19) 0.9 (0.6–1.5) 1.6 (1.1–2.3)
1.2 (0.6–2.3) 0.7 (0.2–1.7) 1.06 (0.86–1.30) 0.82 (0.68–0.98) 0.87 (0.78–1.12)
Former versus never smokers
Quit < 1 year Quit 1 year Duration <20 years 20 years <10 years 10–18 years 19–34 years <10 years 10–19 years 20 years
[l] [l] [f] [f] [f]
1.2 (0.9–1.8) 1.1 (0.7–1.6)
0.9 (0.4–2.2)
0.9 (0.6–1.4) 0.95 (0.84–1.08) 0.87 (0.77–0.97) 1.17 (0.95–1.44) 0.95 (0.87–1.05) 0.5 (0.3–0.6) 0.6 (0.4–1.0) 1.1 (0.7–1.9) 0.9(0.6–1.4) 0.6 (0.4–1.1) 0.94 (0.82–1.09) 0.93 (0.80–1.08) 0.89 (0.74–1.06)
[a] OR for menopausal status, frequency of Pap smears, age at menarche, education, breast-feeding, race, BMI and oral contraceptive use. [b] OR(unconditional stepwise logistic regression analysis) adjusted for age at menarche, age at first and last delivery, parity, number of abortion, age at first marriage, education, breast-feeding, occupation, BMI, family history of myoma, oral contraceptive use and tubal ligation. [c] OR of exposed to the cigarette smoke. [d] OR of never smokers in comparison with ever smokers estimates by univariate logistic regression models. [e] RR. p = 0.018 for linear trend in logistic model. [f] OR adjusted for age, clinic (used for frequency-matching of the study groups), ethnicity, education and marital status. [g] IRR (incidence rate ratios) adjusted for age, time period, age at menarche, parity, age at the first birth, years since last birth, use of oral contraceptives, education, caffeine intake and BMI. [h] OR adjusted for age, age at menarche, full-term pregnancies, BMI and physical activity. First row referred to diffuse myomas, second row referred to submucosal myomas and the third row referred to intramural/subserosal myomas. [i] RR adjusted for race and family history of fibroids and stratified by age. [l] RR adjusted for age, education, parity, contraceptive use and Quetelet’s index. [m] RR adjusted for age, race, marital status, age at menarche, BMI, age at first birth, years since last birth, history of infertility and age at first oral contraceptive use. [n] OR () adjusted for age at sterilization and number of living children. The same results in white women, were obtained in multivariate analysis (adjusted for age, education, heavy menstrual flow, irregular cycles, duration of bleeding, cycle length, number of living children, years since last delivery).
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Fig. 2. Study-specific and summary odds ratios (OR) of uterine myoma for ever smokers versus non smokers. CI: confidence interval.
Finally, no effect was found for moderate and heavy tobacco smoking. The present meta-analysis may be affected by limitations and biases intrinsic in the observational studies included in the metaanalysis. Smoking is self-reported information, thus some misclassification may have occurred. However, information on tobacco smoking in observational studies has been shown to be satisfactorily reproducible and valid [30,31]. An important limitation is that, in some of our meta-analysis estimates, we found large heterogeneity, that could be explained by several reasons: the study design, the absence (at least in ever smokers analysis) of adjusted OR and other characteristics such as different race and the age range of the women included in the studies. As regard the study design, some heterogeneity remained among case–control and cohort studies. Different women race were enrolled in the selected studies and because of sample size limitations, not in all studies were conducted race restricted analyses. Risk factors for
myoma may vary by race like as the incidence of disease [32], number and size of myomas: in an American cross-sectional study of women undergoing premenopausal hysterectomy, African-American women showed higher prevalence of risk factors, such as obesity and hypertension, when compared with White women [33]. In an American case–control study, including White and African-American women, smoking of one or more packs per day increased myoma risk but only for White women [11]. Moreover, there were evidence that premenopausal African-American women have higher ovarian hormone levels than White women [34]. In order to focus on a population most likely to develop uterine myoma, several studies included only premenopausal women [22,23,27] or selected specific age range to cover the reproductive years [9,11–13], but not all. It is possible that menopausal status may modify the relation between smoking and myoma. Furthermore, a small protection in smokers could be explained by anti-estrogenic effects of tobacco smoking.
Fig. 3. Study-specific and summary odds ratios (OR) of uterine myoma for current smokers versus non smokers. CI: confidence interval.
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Fig. 4. Study-specific and summary odds ratios (OR) of uterine myoma for former smokers versus non smokers. CI: confidence interval.
Fig. 5. Study-specific and summary odds ratios (OR) of uterine myoma for moderate (A) and heavy (B) current smokers versus non smokers. CI: confidence interval.
In conclusion, despite these limitations, we believe that this study could be informative about the relationship between cigarette smoking and uterine myoma. As a matter of fact, the overall estimates showed the same direction of no significant association, except for a weak protection that, given the limited number of studies and the absence of a dose dependent effect, deserves further investigations. References
Fig. 6. Funnel-plot of studies on tobacco smoking and risk of uterine myoma. OR: odds ratios for ever smokers versus non smokers; CI: confidence interval; s.e.: standard error.
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F. Chiaffarino et al. / European Journal of Obstetrics & Gynecology and Reproductive Biology 197 (2016) 63–71 [6] Baron JA, La Vecchia C, Levi F. The antiestrogenic effect of cigarette smoking in women. Am J Obstet Gynecol 1990;162:502–14. [7] Lumbiganon P, Rugpao S, Phandhu-fung S, Laopaiboon M, Vudhikamraksa N, Werawatakul Y. Protective effect of depot-medroxyprogesterone acetate on surgically treated uterine leiomyomas: a multicentre case–control study. Br J146?Obstet Gynaecol 1996;103:909–14. [8] Parazzini F, Negri E, La Vecchia C, et al. Uterine myomas and smoking. Results from an Italian study. J Reprod Med 1996;41:316–20. [9] Ross RK, Pike MC, Vessey MP, Bull D, Yeates D, Casagrande JT. Risk factors for uterine fibroids: reduced risk associated with oral contraceptives. Br Med J146?(Clin Res Ed) 1986;293:359–62. [10] Templeman C, Marshall SF, Clarke CA, et al. Risk factors for surgically removed fibroids in a large cohort of teachers. Fertil Steril 2009;92:1436–46. [11] Chen CR, Buck GM, Courey NG, Perez KM, Wactawski-Wende J. Risk factors for uterine fibroids among women undergoing tubal sterilization. Am J Epidemiol 2001;153:20–6. [12] Dragomir AD, Schroeder JC, Connolly A, et al. Potential risk factors associated with subtypes of uterine leiomyomata. Reprod Sci 2010;17:1029–35. [13] Marshall LM, Spiegelman D, Manson JE, et al. Risk of uterine leiomyomata among premenopausal women in relation to body size and cigarette smoking. Epidemiology 1998;9:511–7. [14] Wise LA, Palmer JR, Harlow BL, et al. Risk of uterine leiomyomata in relation to tobacco, alcohol and caffeine consumption in the Black Women’s Health Study. Hum Reprod 2004;19:1746–54. [15] Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 2009;6:e1000097. [16] Stroup DF, Berlin JA, Morton SC, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA 2000;283:2008–12. [17] Wells GA. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomized studies in meta-analyses. http://www.ohri.ca/programs/ clinical_epidemiology/oxford.asp [Last accessed 20.10.15]. [18] Hamling J, Lee P, Weitkunat R, Ambuhl M. Facilitating meta-analyses by deriving relative effect and precision estimates for alternative comparisons from a set of estimates presented by exposure level or disease category. Stat Med 2008;27:954–70. [19] Greenland S. Quantitative methods in the review of epidemiologic literature. Epidemiol Rev 1987;9:1–30. [20] Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med 2002;21:1539–58.
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[21] Thornton A, Lee P. Publication bias in meta-analysis: its causes and consequences. J Clin Epidemiol 2000;53:207–16. [22] Nagata C, Nakamura K, Oba S, Hayashi M, Takeda N, Yasuda K. Association of intakes of fat, dietary fibre, soya isoflavones and alcohol with uterine fibroids in Japanese women. Br J Nutr 2009;101:1427–31. [23] Bidgoli SA, Khorasani H, Keihan H, Sadeghipour A, Mehdizadeh A. Role of endocrine disrupting chemicals in the occurrence of benign uterine leiomyomata: special emphasis on AhR tissue levels. Asian Pac J Cancer Prev 2012;13:5445–50. [24] Lambertino A, Turyk M, Anderson H, Freels S, Persky V. Uterine leiomyomata in a cohort of Great Lakes sport fish consumers. Environ Res 2011;111: 565–72. [25] Pakiz M, Potocnik U, But I. Solitary and multiple uterine leiomyomas among Caucasian women: two different disorders. Fertil Steril 2010;94:2291–5. [26] Samadi AR, Lee NC, Flanders WD, Boring 3rd JR, Parris EB. Risk factors for selfreported uterine fibroids: a case-control study. Am J Public Health 1996;86: 858–62. [27] Faerstein E, Szklo M, Rosenshein N. Risk factors for uterine leiomyoma: a practice-based case-control study I. African-American heritage, reproductive history, body size, and smoking. Am J Epidemiol 2001;153:1–10. [28] Borenstein M. In: Rothstein HR, Sutton AJ, Borenstein M, editors. Publication Bias in Meta-Analysis–Prevention Assessment and Adjustments. London, UK: Wiley; Software for publication bias; 2005. p. 193–220. [29] Cochrane. http://handbo ok.cochrane.org/ch apter_10/10_4_3_1_ recommendations_on_testing_for_funnel_plot_asymmetry.htm (Last accessed 22.10.15). [30] D’Avanzo B, La Vecchia C, Katsouyanni K, Negri E, Trichopoulos D. Reliability of information on cigarette smoking and beverage consumption provided by hospital controls. Epidemiology 1996;7:312–5. [31] Patrick DL, Cheadle A, Thompson DC, Diehr P, Koepsell T, Kinne S. The validity of self-reported smoking: a review and meta-analysis. Am J Public Health 1994;84:1086–93. [32] Baird DD, Dunson DB, Hill MC, Cousins D, Schectman JM. High cumulative incidence of uterine leiomyoma in black and white women: ultrasound evidence. Am J Obstet Gynecol 2003;188:100–7. [33] Moorman PG, Leppert P, Myers ER, Wang F. Comparison of characteristics of fibroids in African American and white women undergoing premenopausal hysterectomy. Fertil Steril 2013;99:768–76. e1. [34] Haiman CA, Pike MC, Bernstein L, et al. Ethnic differences in ovulatory function in nulliparous women. Br J Cancer 2002;86:367–71.
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European Journal of Obstetrics & Gynecology and Reproductive Biology journal homepage: www.elsevier.com/locate/ejogrb
Cigarette smoking and risk of uterine myoma: systematic review and meta-analysis Francesca Chiaffarino a,*, Elena Ricci a, Sonia Cipriani a, Vito Chiantera b, Fabio Parazzini a,c a
Dipartimento della donna, del bambino e del neonato, Fondazione IRCCS Ca` Granda, Ospedale Maggiore Policlinico, Milan, Italy Department of Gynecology, Charite` Universita¨tsmedizin, Charitestraße 1, 10117 Berlin, Germany c Dipartimento di Scienze Cliniche e di Comunita`, Universita` degli studi di Milano, Milan, Italy b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 22 June 2015 Received in revised form 11 November 2015 Accepted 18 November 2015
Objective: To review the literature on the relationship between smoking and the risk of uterine myoma, we conducted a systematic review and a meta-analysis of published studies. In this meta-analysis, we included all identified studies of association between smoking and uterine myoma where these were case–control or cohort studies, reporting original data, ultrasound or histological confirmed diagnosis of myomas and information on the association between tobacco smoking and myomas. Study design: We carried out a literature search on MEDLINE/EMBASE of all studies published as original articles in English up to October 2015, using the Medical Subject Heading terms and free search terms about myoma and smoking. We selected only studies published in English. Moreover, bibliographies of the retrieved papers were reviewed, to identify any other relevant publication. A total of 14 different studies were eligible for a qualitative synthesis and data extract from 10 studies were combined in a meta-analysis. Results: The summary OR of former compared to never smokers was 0.93 (0.88–0.99) with no heterogeneity. The summary OR of current smokers compared to never smokers, was 0.83 (0.65–1.04), even if the subtotal OR in cohort studies was 0.85 (0.73–0.98) with no heterogeneity. When sensitivity analysis was performed the summary OR was 0.83 (0.71–0.97). Conclusion: The primary meta-analyses found no significant effect of smoking on risk of uterine myoma. Subgroup analysis for study design showed a small risk reduction for current and former smokers in cohort studies. A sensitivity analysis showed an inverse association between ever smoking and uterine myoma. However, given the limited number of studies in each sub-analysis, weak associations and the absence of a dose dependent effect, caution should be paid in the interpretation of these findings and further investigation are needed. ß 2015 Elsevier Ireland Ltd. All rights reserved.
Keywords: Smoking Uterine myoma Risk factor
Introduction Uterine myomas are the most common benign tumors derived from smooth muscle cells in the uterine myometrium. In an online survey the self-reported prevalence of myoma, in the age group of 40–49 years, ranged from 9.4% in United Kingdom to 17.4% in Italy [1], but many tumors are asymptomatic and may not be diagnosed. Although the etiology of uterine myoma is still not well known,
* Corresponding author at: Via Commenda, 12-20122 Milano, Italy. Tel.: +39 02 55032318; fax: +39 02 550320252. E-mail address: [email protected] (F. Chiaffarino). http://dx.doi.org/10.1016/j.ejogrb.2015.11.023 0301-2115/ß 2015 Elsevier Ireland Ltd. All rights reserved.
advances have been made in the understanding of the hormonal factors, genetic factors and growth factors of these tumors [2]. They have considered a hormonal-dependent pathological condition, where growth is thought to depend on ovarian hormones. Both estrogen and progesterone appear to promote the development of myomas. Factors that increase exposure to estrogen, such as obesity and early menarche, increase the incidence [2]. On the other hand, exercise and increased parity, which decreased exposure to estrogen, appear to be protective [3]. Smoking is a modifiable risk factor that may affect endogenous levels of hormones and women who smoke have lower urinary estrogen levels during the luteal phase of the menstrual cycle than non-smokers [4]. Nicotine can reduce androgens conversion to
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estrone secondary to inhibition of aromatase [5]. Thus, smoking is associated with impaired production and reduced levels of endogenous circulating estrogens [6]. Epidemiological studies investigating the role of tobacco smoking have shown conflicting results: some have shown an inverse relationship between cigarette smoking and risk of uterine myoma [7–10], but in others smoking increased the risk [11,12], whereas in two cohort studies it was unrelated to myoma risk [13,14]. Thus, in order to provide a summary of the available literature on the relation between cigarette smoking and uterine myoma, we conducted a systematic review and to allow an overall quantitative estimate of any such relation, we combined in a meta-analysis all published data on the issue. Materials and methods The review and the meta-analysis were performed according to PRISMA (preferred reporting items for systematic reviews and meta-analyses) [15] and MOOSE (Meta-analysis of Observational Studies in Epidemiology) guidelines [16]. We executed a MEDLINE/EMBASE search of papers published until October 10, 2015, using the Medical Subject Heading terms in free research ‘‘myoma’’ or ‘‘leiomyoma’’ combined with ‘‘smoking’’ and ‘‘tobacco’’ and free search terms ‘‘tobacco’’ or ‘‘smoking’’ or ‘‘smok*’’ or ‘‘cigarette*’’ in combination with ‘‘fibroids’’ or ‘‘uterine fibroids’’ or ‘‘myoma’’ or ‘‘uterine myoma’’ or ‘‘leiomyoma’’ or ‘‘uterine leiomyoma’’. We selected only studies on humans, published as full-length papers in English. Moreover, bibliographies of the retrieved papers were reviewed, to identify any other relevant publication. In the review we included all identified studies of association of smoking and uterine myoma, whereas studies were included in the meta-analysis only if: they were case–control or cohort studies, reporting original data; diagnosis of myomas was ultrasound or histological confirmed and/or clinically based; studies reported information on the association between tobacco smoking and myomas, including estimates of the relative risk (RR) or the odds ratio (OR), with the corresponding 95% confidence interval (CI), or frequency distribution to calculate them. When we found more than one publication based on the same study population and data, we included only the one with most detailed information, or published most recently. Data extraction and selection of eligible studies was carried out in duplicate by two investigators (FC and ER). Disagreements were solved by discussing and reviewing the respective issue. Crossreferencing of selected articles revealed no further eligible records. From each publication we extracted the following information: country of origin; study design; number and characteristics of subjects (cases, controls or cohort size); age, if available; categories of tobacco smoking (smoking status, smoking intensity and duration of smoking, if available); measures of association (RR or OR) of myomas and corresponding 95% CI for every category of tobacco smoking, or frequency distribution to calculate them; confounding variables allowed for in the statistical analysis. When more than one regression model was provided, estimates adjusted for the largest number of confounding variables were considered. The quality of the studies included in the meta-analysis were assessed using the Newcastle–Ottawa scale. This instrument was developed to assess the quality of nonrandomized studies, specifically cohort and case–control studies [17]. Studies were judged based on three broad categories: selection of study groups, comparability of study groups, and assessment of outcome (cohort studies) or ascertainment of exposure (case–control studies). Maximum score was 9.
For some studies, we pooled estimates of different categories of cases or controls using the method by Hamling et al. [18], thus taking into account their correlation. Pooled estimates of the odds ratios (OR) and the corresponding 95% confidence intervals (CI) were calculated using fixed or, when significant heterogeneity among estimates emerged, random effects models. Sensitivity analysis were also performed. We assessed the heterogeneity among studies using the x2 test [19] and quantified it using the I2 statistic, which represents the percentage of the total variation across studies that is attributable to heterogeneity rather than chance [20]. Results were defined as heterogeneous for p values less than 0.10 [19]. We computed summary estimates for ever tobacco smokers, former smokers, current smokers, moderate current smokers, and heavy current smokers, as compared to never smokers. Among the selected studies, six reported more categories of current smokers, thus we could calculate separate estimates for moderate and heavy current smokers but we were able to combine data from four studies because two studies considered ever smokers and not only current ones. Moreover, different cut-points for moderate and heavy smoking were chosen, depending on those shown in the papers: thus the cut-point for moderate smoking was less than 10 cigarettes per day in two studies [8,14], less than 15 cigarettes per day in one study [13] and less than 1 pack/day in another [11]. For heavy current smokers the cut-point was more than 19 cigarettes per day in two studies [11,14], more than 24 in one study [13] and more than 10 in another one [8]. Publication bias was evaluated using funnel plot [21]. Results From the literature search we identified 345 articles, after the exclusion of 170 as duplicates. 331 studies were excluded for the reason shown in Fig. 1 and 14 articles describing 14 different studies were eligible for a qualitative synthesis and data extract from 10 studies were combined in a meta-analysis. The main characteristics of identified papers are presented in Table 1: eight case control studies, four cohort studies and two cross-sectional. Of the selected studies, 8 were from USA, 3 from Europe and 3 from Asia. The articles were published between 1986 and 2012. The effect estimates according to smoking exposure published in the selected articles were summarized in Table 2. In the meta-analysis we excluded two cross-sectional studies [12,22], since in this study design exposure and disease are recorded at the same time: we could not determine whether the exposure preceded the occurrence of uterine myoma. Moreover, two studies were excluded because the categories of smoking exposure were not clear [23,24] and in the American cohort study the presence of myoma was self-reported without any other diagnosis confirmation [24]. Overall, data from ten studies, including 374,212 women, 7612 with uterine myoma, were used in the meta-analysis. Ever smokers In qualitative analysis seven studies reported information on ever smokers (Table 2). Among these, three of them, two case– control studies and one cohort study, showed no effect of ever smoking. Two case–control studies showed a protective effect of ever smoking [7,9] and in the American study was dose dependent [9] whereas the exposure to cigarette smoking increased the risk of myoma in Iranian premenopausal women and in Slovenian women [23,25]. In quantitative analysis the Iranian study was excluded because the categories of smoking exposure were not clear. In the random
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Fig. 1. Flow chart of the selection of studies on cigarette smoking and risk of uterine myoma included in the systematic review and meta-analysis.
effects model of the meta-analysis, we considered ten studies. Only two studies [7,26] reported the adjusted OR of ever smokers compared to never smokers; in the study by Pakiz we calculated the OR from published frequency distribution; seven studies did not compare ever versus never smokers, thus we calculated the OR or RR, as appropriate, combining former and current smokers or more categories of ever smokers in one ever smokers category. Consequently, these estimates were not adjusted. The summary OR (95% CI) of myoma for ever smokers, compared to never smokers, was 0.86 (0.73–1.01) with significant heterogeneity (x2 = 76.92, p = 0.00001), similarly to both OR obtained from cohort and from case–control studies separately, as shown in Fig. 2. Moreover, as regards the case–control studies on the relationship between ever smoking and uterine myoma, it becomes immediately evident, that the OR in the study by Pakiz [25] was completely different. It should take into account that this Slovenian study [25] regarded a small sample of women with higher age range when compared with other studies and the estimate, included in the meta-analysis, was not adjusted. Furthermore, tobacco smoking was not the main topic of the paper. In sensitivity analysis, when a
Slovenian study [25] was dropped, the OR of myoma for ever smokers in case–control studies subgroup was 0.75 (0.62–0.92), and the summary OR for ever smokers as compared to never smokers was 0.83 (0.71–0.97) with significant heterogeneity (x2 = 18.02, p = 0.003). Current smokers A cohort study [10] and a case–control study [8], including preand post-menopausal women, showed an inverse association between myoma and smoking. On the contrary, in a Chinese case– control study, current smoking of one or more packs of cigarettes per day was associated with an increasing risk for White women, but not for African-American women [11]. Moreover, in a crosssectional study, smoking was positively associated with diffuse myomas, with similar patterns between African-American and Caucasians women, but was not associated with submucosal or intramural/subserosal myoma [12]. In a case–control [27] and two cohort studies [13,14] current smoking was not associated with risk of uterine myoma (Fig. 3).
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Table 1 Main characteristics of the studies on tobacco smoking and risk of uterine myoma. Country
Study design
Cases
Controls
Sample size cases/ controls
Age (ys)
Smoking habit
Bidgoli et al., 2012
Iran
Case–control
Women with myomas confirmed by pathological reports
Women without myomas
138/138
Postmenopausal women were excluded.
Chen et al., 2001
USA
Case–control
Women with myomas undergoing tubal sterilization
Women without myomas undergoing tubal sterilization
White: 247–988, African-American: 70– 280 cases and controls respectively
Dragomir et al., 2010
USA
Cross-sectional
Women with myomas (ultrasound confirmed)
Women without myomas
Faerstein et al., 2001
USA
Case–control
Women with myomas (surgically or sonographically confirmed)
Women without myomas
Caucasian: 203–202, African-American: 419–162 cases and controls respectively 318/394
<44 were included. White: 35.6–32.0 African-American: 34.1–29.5 cases and controls respectively 35–49
Active and passive exposure to cigarettes smoke versus no exposure Never, former smoker, current smoker smoking < or >1 cigarettes pack/day
Lambertino et al., 2011
USA
Cohort
Women with self reported myomas
Women without myomas
95 cases
Lumbiganon et al., 1995
Thailand
Case–control
Women without myomas
910/2709
Marshall et al., 1998
USA
Cohort
Women with myomas who required surgical treatment (histopathologically confirmed) Women with myomas (ultrasound/ hysterectomy confirmed)
Women without myomas
2967 new cases among 94,095 premenopausal women
Nagata et al., 2009
Japan
Cross-sectional
Women without myomas
Pakiz et al., 2010
Slovenia
Case–control
Parazzini et al., 1995
Italy
Case–control
Women undergoing vaginal hysterectomy for pelvic organ prolapse Women admitted to the hospitals for acute, nongynecologic, nonhormonal, nonneoplastic conditions
Women with myomas (ultrasound/ hysterectomy confirmed) Women with myomas (histologically confirmed)
Women with myomas (histologically confirmed)
42.3 6.4 20–55 cases; 39.8 6.0 18–53 controls. Postmenopausal women were excluded. Mean age women with myoma: 54.7. w/o myoma:52.7 87.5% and 82.7% cases and controls respectively were <50 age.
Never, ever smokers
Never, former, current smoker. Tertile of duration and pack/ years
Confounding factors
NOS Quality score
Age at sterilization and number of living children, education, heavy menstrual flow, irregular cycles, cycle length Age, age at menarche, full term pregnancies, BMI, Physical activity, ethnicity Age, clinic, etnicity, education and marital status
6
6
% cigarette smoking
N.A.
never, ever smokers
Control for potential counfounding factors, but not otherwise specified
6
25–42
Never, current, former smokers, n. cigarettes/ day
8
54/231
Postmenopausal women were excluded
Never, current, former smokers
Age, race, marital status, age at menarche, BMI, age at first birth, years since last birth, history of infertility and age at first oral contraceptive use N.A.
50 + 56/41
Age range. Cases: 42.4–53.5 Controls: 57.2–64.2
smoking and duration of smoking (mean)
N.A.
5
476/1283
<55 years
Never, current, former smokers, no. of cigarettes/day, duration of smoking (years)
Age, education, menopausal status, BMI, parity, oral contraceptive use
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Study and year
8 Age, time period, age at menarche, parity, age at first and last birth, education, BMI, OC use
6
Never, former, current smoker. Never, former, current smoker. Cigarettes/day, pack/years, y. of smoking, y. since quitting 25–84
1790 cases among 133,000 women 2177 cases Wise et al., 2004
Cohort USA
Templeman et al., 2009
Cohort USA
Samadi et al., 1996
Case–control
Women with myomas (surgically confirmed) Women with myomas (ultrasound or hysterectomy confirmed)
Women without myomas Women without myomas
Only premenopausal women
6 20–54 201/1503 Women without myomas
Never, ever
25–39 535/535 Ross et al., 1986
England and Scotland USA
Case–control
Women with myomas (pathologically confirmed) Women with selfreported myomas with physician clinical diagnosis
Women without myomas
No. of cigarettes/day
Menopausal status, frequency pap test, age at menarche, education, breastfeeding, race, BMI, oral contraceptive use
6
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In the meta-analyses of three cohort and three case–control studies combined, the summary OR of current smokers was 0.83 (0.65–1.04) with significant heterogeneity (x2 = 24.48, p = 0.0004). The subtotal OR from cohort studies was 0.85 (0.73–0.98) with no heterogeneity (x2 = 3.60, p = 0.16). If data were published in classes of cigarettes per day or pack/years, we included the estimates of the highest dose class in the analyses. Former smokers Six studies reported information on former smokers (Table 2) and only a cohort study [10] found a small significant protective effect of former smoking on myoma risk. In the meta-analysis the summary OR of former compared to never smokers (see Fig. 4) was 0.93 (0.88–0.99) with not significant heterogeneity (x2 = 4.03, p = 0.67). Moderate and heavy current smokers Among the selected studies, six reported more categories of current smokers, thus we could calculate separate estimates for moderate and heavy current smokers but we were able to combine data from four studies because two studies considered ever smokers and not only current ones. Moreover, different cut-points for moderate and heavy smoking were chosen, depending on those shown in the papers (see Materials and Methods). Fig. 5A and B reported respectively the results of the metaanalyses of low-moderate (including the risk estimates for the lowest class) and heavy current smokers (including the estimates for the highest class) versus never smokers: both ORs were not statistically significant with borderline heterogeneity. Duration of smoking Only three studies reported effect measures for the relationship between duration of smoking and myoma risk. Two of them found no association, whereas no clear association emerged between years of smoking and risk of myoma in our previous [8]: women with less than twenty years of smoking, had approximately a 50% reduced risk of myoma requiring surgery, but this protection was not found in women with twenty or more years of smoking. Since information on smoking habit duration was even more heterogeneous than that on cigarettes dose, we decided not to try a quantitative synthesis. Funnel plot, reported in Fig. 6, is a graphical tool for the assessment of publication bias. Its interpretation is largely subjective [28] and can be supported by Egger’s test for assessing asymmetry. In the present study the Egger’s test result (p = 0.35) suggested no presence of publication bias thought, because of the lower power of the test, bias cannot be excluded [29]. Comment Epidemiological studies investigating the role of smoking on risk of myoma have shown conflicting results but to our knowledge, this is the first systematic review and meta-analysis on that topic. The primary meta-analysis found no significant effect of smoking on risk of myoma. Subgroup analysis for study design showed a small, but significant, risk reduction for current and former smokers in cohort studies. The absence of statistical significance in ever smokers and in the subgroup of case–control studies could be due to the analysis, which was based, in most studies, in absence of adjusted OR, on raw estimates. Moreover, as regards the case– control studies on the relationship between ever smoking and myoma, a sensitivity analysis, dropping the study by Pakiz [25], showed an inverse association between ever smoking and myoma.
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Table 2 Reported estimates in selected studies. Comparison
First author
Year
Study design
Published estimates (95% CI) All
Samadi [a] Lumbiganon [b] Bidgoli [c] Pakiz [d] Ross [e] Ross [e] Faerstein [f] Faerstein [f] Faerstein [f] Wise [g] Wise [g] Wise [g] Wise [g]
1996 1996 2012 2010 1986 1986 2001 2001 2001 2004 2004 2004 2004
Case–control Case–control Case–control Case–control Case–control Case–control Case–control Case–control Case–control Cohort Cohort Cohort Cohort
0.8 (0.5–1.1) 0.63 (0.40–0.99) 2.09 (1.29–3.37) 0.198 (0.075–0.52) 0.74 0.65 1.0 (0.6–1.6) 0.8 (0.5–1.4) 0.9 (0.5–1.4)
Faerstein [f] Dragomir [h] Dragomir [h] Dragomir [h] Wise [g] Templeman [i] Parazzini [l] Parazzini [l] Marshall [m] Marshall [m] Marshall [m] Chen [n] Chen [n] Wise [g] Wise [g] Wise [g]
2001 2010 2010 2010 2004 2009 1996 1996 1998 1998 1998 2001 2001 2004 2004 2004
Case–control Cross-sectional Cross-sectional Cross-sectional Cohort Cohort Case–control Case–control Cohort Cohort Cohort Case–control Case–control Cohort Cohort Cohort
0.7 (0.4–1.3) 2.13 (1.29–3.53) 1.24 (0.74–2.07) 0.87 (0.58–1.32)
Parazzini [l] Chen [n] Faerstein [f] Wise [g] Templeman [i] Marshall [m] Marshall [m]
1996 2001 2001 2004 2009 1998 1998
Case–control Case–control Case–control Cohort Cohort Cohort Cohort
Parazzini Parazzini Faerstein Faerstein Faerstein Wise [g] Wise [g] Wise [g]
1996 1996 2001 2001 2001 2004 2004 2004
Case–control Case–control Case–control Case–control Case–control Cohort Cohort Cohort
Published estimates (95% CI) White women
Published estimates (95% CI) African-American women
Ever versus never smokers
1–14 cigarettes/day 15 cigarettes/day <5 pack/year 5–13 pack/year 13.2–58 pack/year <2.0 pack/year 2.0–5.9 pack/year 6.0–12.4 pack/year 12.5 pack/year
0.94 0.97 0.88 0.93
(0.79–1.12) (0.81–1.16) (0.73–1.04) (0.79–1.11)
1.97 1.13 0.89 0.88
(1.11–3.51) (0.64–2.01) (0.56–1.40) (0.77–1.01)
Current versus never smokers
1–9 cigarettes/day 10 cigarettes/day 1–14 cigarettes/day 15–24 cigarettes/day 25 cigarettes/day <1 pack/day 1 pack/day <10cigarettes/day 10–19 cigarettes/day 20 cigarettes/day
3.00 (1.07–8.38) 1.60 (0.52–4.98) 0.63 (0.23–1.74)
0.65 (0.50–0.85) 0.5 (0.3–0.7) 0.5 (0.4–0.8) 0.84 (0.70–1.01) 0.89 (0.75–1.06) 0.93 (0.73–1.19) 0.9 (0.6–1.5) 1.6 (1.1–2.3)
1.2 (0.6–2.3) 0.7 (0.2–1.7) 1.06 (0.86–1.30) 0.82 (0.68–0.98) 0.87 (0.78–1.12)
Former versus never smokers
Quit < 1 year Quit 1 year Duration <20 years 20 years <10 years 10–18 years 19–34 years <10 years 10–19 years 20 years
[l] [l] [f] [f] [f]
1.2 (0.9–1.8) 1.1 (0.7–1.6)
0.9 (0.4–2.2)
0.9 (0.6–1.4) 0.95 (0.84–1.08) 0.87 (0.77–0.97) 1.17 (0.95–1.44) 0.95 (0.87–1.05) 0.5 (0.3–0.6) 0.6 (0.4–1.0) 1.1 (0.7–1.9) 0.9(0.6–1.4) 0.6 (0.4–1.1) 0.94 (0.82–1.09) 0.93 (0.80–1.08) 0.89 (0.74–1.06)
[a] OR for menopausal status, frequency of Pap smears, age at menarche, education, breast-feeding, race, BMI and oral contraceptive use. [b] OR(unconditional stepwise logistic regression analysis) adjusted for age at menarche, age at first and last delivery, parity, number of abortion, age at first marriage, education, breast-feeding, occupation, BMI, family history of myoma, oral contraceptive use and tubal ligation. [c] OR of exposed to the cigarette smoke. [d] OR of never smokers in comparison with ever smokers estimates by univariate logistic regression models. [e] RR. p = 0.018 for linear trend in logistic model. [f] OR adjusted for age, clinic (used for frequency-matching of the study groups), ethnicity, education and marital status. [g] IRR (incidence rate ratios) adjusted for age, time period, age at menarche, parity, age at the first birth, years since last birth, use of oral contraceptives, education, caffeine intake and BMI. [h] OR adjusted for age, age at menarche, full-term pregnancies, BMI and physical activity. First row referred to diffuse myomas, second row referred to submucosal myomas and the third row referred to intramural/subserosal myomas. [i] RR adjusted for race and family history of fibroids and stratified by age. [l] RR adjusted for age, education, parity, contraceptive use and Quetelet’s index. [m] RR adjusted for age, race, marital status, age at menarche, BMI, age at first birth, years since last birth, history of infertility and age at first oral contraceptive use. [n] OR () adjusted for age at sterilization and number of living children. The same results in white women, were obtained in multivariate analysis (adjusted for age, education, heavy menstrual flow, irregular cycles, duration of bleeding, cycle length, number of living children, years since last delivery).
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Fig. 2. Study-specific and summary odds ratios (OR) of uterine myoma for ever smokers versus non smokers. CI: confidence interval.
Finally, no effect was found for moderate and heavy tobacco smoking. The present meta-analysis may be affected by limitations and biases intrinsic in the observational studies included in the metaanalysis. Smoking is self-reported information, thus some misclassification may have occurred. However, information on tobacco smoking in observational studies has been shown to be satisfactorily reproducible and valid [30,31]. An important limitation is that, in some of our meta-analysis estimates, we found large heterogeneity, that could be explained by several reasons: the study design, the absence (at least in ever smokers analysis) of adjusted OR and other characteristics such as different race and the age range of the women included in the studies. As regard the study design, some heterogeneity remained among case–control and cohort studies. Different women race were enrolled in the selected studies and because of sample size limitations, not in all studies were conducted race restricted analyses. Risk factors for
myoma may vary by race like as the incidence of disease [32], number and size of myomas: in an American cross-sectional study of women undergoing premenopausal hysterectomy, African-American women showed higher prevalence of risk factors, such as obesity and hypertension, when compared with White women [33]. In an American case–control study, including White and African-American women, smoking of one or more packs per day increased myoma risk but only for White women [11]. Moreover, there were evidence that premenopausal African-American women have higher ovarian hormone levels than White women [34]. In order to focus on a population most likely to develop uterine myoma, several studies included only premenopausal women [22,23,27] or selected specific age range to cover the reproductive years [9,11–13], but not all. It is possible that menopausal status may modify the relation between smoking and myoma. Furthermore, a small protection in smokers could be explained by anti-estrogenic effects of tobacco smoking.
Fig. 3. Study-specific and summary odds ratios (OR) of uterine myoma for current smokers versus non smokers. CI: confidence interval.
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F. Chiaffarino et al. / European Journal of Obstetrics & Gynecology and Reproductive Biology 197 (2016) 63–71
Fig. 4. Study-specific and summary odds ratios (OR) of uterine myoma for former smokers versus non smokers. CI: confidence interval.
Fig. 5. Study-specific and summary odds ratios (OR) of uterine myoma for moderate (A) and heavy (B) current smokers versus non smokers. CI: confidence interval.
In conclusion, despite these limitations, we believe that this study could be informative about the relationship between cigarette smoking and uterine myoma. As a matter of fact, the overall estimates showed the same direction of no significant association, except for a weak protection that, given the limited number of studies and the absence of a dose dependent effect, deserves further investigations. References
Fig. 6. Funnel-plot of studies on tobacco smoking and risk of uterine myoma. OR: odds ratios for ever smokers versus non smokers; CI: confidence interval; s.e.: standard error.
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